Quickstart

This workflow generates a tiny deterministic FPM acquisition in memory and reconstructs it. It needs no downloaded data or machine-specific path. For unfamiliar optics terms, keep the FPM glossary open alongside the example.

import numpy as np
import fpm_rs as fpm

# All distances are metres. Array shapes are (height, width).
optics = fpm.Optics(
    wavelength_vacuum_m=532e-9,
    objective_na=0.10,
    magnification=4.0,
    camera_pixel_size=6.5e-6,
)
geometry = fpm.PlanarLEDArray(
    shape=(3, 3),
    pitch_m=4e-3,
    reference_index=(1.0, 1.0),
    pose=fpm.ArrayPose.from_translation(
        translation_m=(0.0, 0.0, -90e-3),
    ),
)
illumination = fpm.Illumination(geometry=geometry)
model = fpm.compile_model(optics, illumination, image_shape=(32, 32))

row, column = np.indices(model.reconstruction_shape)
amplitude = 0.7 + 0.3 * ((row // 8 + column // 8) % 2)
phase = 0.4 * np.sin(row / 7.0) * np.cos(column / 9.0)
object_field = np.asarray(amplitude * np.exp(1j * phase), dtype=np.complex128)

simulation = fpm.simulate(model, object_field, seed=1234)
problem = fpm.ReconstructionProblem(
    simulation.measurements,
    simulation.reconstruction_model,
    name="quickstart",
)
result = fpm.AlternatingProjection(iterations=20).run(problem)

print(result.amplitude.shape)
print(result.runtime.completed_iterations)
print(result.final_objective)

Expected output includes an amplitude shape of (42, 42), 20 completed iterations, and a finite final objective. Exact floating-point objective values may vary slightly by platform.

The model separates the measured low-resolution frame shape from the recovered object shape. simulate returns both detector intensities and the reconstruction model appropriate for those intensities. The algorithm returns NumPy amplitude, phase, spectrum, and pupil arrays.

The default reconstruction grid works for this example; see Choose the reconstruction shape for exact sizing, the three automatic strategies, and the synthetic-NA heuristic's limits.

Next, follow the rendered first reconstruction tutorial, learn how to record diagnostics, or consult the Python API. For acquired arrays rather than a simulation, see Measurements and acquisitions.